This article critically examines McTaggart’s Paradox, which argues for the unreality of time based on inherent contradictions in the A-series and B-series of temporal order. McTaggart’s assertion that time is unreal stems from the premise that genuine change, essential for time, occurs within the A-series, where events transition from future to present to past. He contends that this series is inherently contradictory, as it requires events to simultaneously possess mutually exclusive properties of past, present, and future. This paradoxical nature leads (...) McTaggart to conclude that time is an illusion. The article challenges McTaggart’s conclusions by advocating for Presentism, the view that only the present moment exists. Presentism is supported by common-sense views and phenomenological experiences of time, which emphasize the experiential privilege of the present. By focusing on the reality of the present moment, Presentism offers a coherent and intuitive understanding of temporal experience, countering McTaggart’s paradox. Additionally, the article explores arguments from philosophers such as Sydney Shoemaker and William Rowe, who challenge McTaggart’s linkage of time with change. Shoemaker proposes that time can exist without change, while Rowe critiques the necessity of change for the existence of time. Through a comprehensive evaluation of McTaggart’s premises and the phenomenological evidence supporting Presentism, this article aims to refute the paradox and affirm the reality of time as experienced through the ever-present ‘Presentness.’ Presentism is thus presented as a robust framework for understanding time, consistent with both philosophical reasoning and everyday temporal experience. (shrink)

Abstract: Breast cancer is reported to be the most common cancer type among women worldwide and it is the second highest women fatality rate amongst all cancer types. Notwithstanding all the progresses made in prevention and early intervention, early prognosis and survival prediction rates are still not sufficient. In this paper, we propose an ANN model which outperforms all the previous supervised learning methods by reaching 99.57 in terms of accuracy in Wisconsin Breast Cancer dataset. Experimental results on Haberman’s Breast (...) Cancer Survival dataset show the superiority of proposed method by reaching 88.24 % in terms of accuracy. The results are the best reported ones obtained from Artificial Neural Network using JNN environment without any preprocessing of the dataset. (shrink)